We show that simple neural network models can predict the range of an overwater emitter in certain ducting environments using physics-based synthetic data and real-world collected data. Evaporative ducting provides a medium through which electromagnetic fields propagate further than in a free-space environment, and with significant multipath effects. The richness of the multipath signal and its range dependence allow for predictive models of emitter localization. We show the limitation of these neural networks in predicting range on real-world collected data in which ducting was apparent but its characteristics unknown.

We design a neural network model of first language acquisition to explore the relationship between child and adult speech sounds. The model learns simple vowel categories using a produce-and-perceive babbling algorithm in addition to listening to ambient speech. The model is similar to that of Westermann & Miranda (2004), but adds a dynamic aspect in that it adapts in both the articulatory and acoustic domains to changes in the child’s speech patterns. The training data is designed to replicate infant speech sounds and articulatory configurations. By exploring a range of articulatory and acoustic dimensions, we see how the child might learn to draw correspondences between his or her own speech and that of a caretaker, whose productions are quite different from the child’s. We also design an imitation evaluation paradigm that gives insight into the strengths and weaknesses of the model. Index Terms: language acquisition, neural networks, selforganizing maps, language development

In recent studies, we and others have found that conditional random fields (CRFs) can be effectively used to perform phone classification and recognition tasks by combining non-Gaussian distributed representations of acoustic input. In previous work by I. Heintz (latent phonetic analysis: Use of singular value decomposition to determine features for CRF phone recognition,Proc.ICASSP, pp. 4541-4544, 2008), we experimented with combining phonological feature posterior estimators and phone posterior estimators within a CRF framework; we found that treating posterior estimates as terms in a ldquophoneme information retrievalrdquo task allowed for a more effective use of multiple posterior streams than directly feeding these acoustic representations to the CRF recognizer. In this paper, we examine some of the design choices in our previous work, and extend our results to up to six acoustic feature streams. We concentrate on feature design, rather than feature selection, to find the best way of combining features for introduction into a log-linear model. We improve upon our previous work to find that several different dimensionality reduction techniques (SVD, PARAFAC2, KLT), followed by a nonlinear transform provided by a multilayer perceptron, provides a significant gain in phone recognition accuracy on the TIMIT task.

We exploit an analogy between document retrieval and phone recognition, and adapt the method of latent semantic analysis for the latter task. By mapping into a space of reduced dimensionality, we hope to uncover previously unexploited relationships between posterior estimates of phonetic events and the parts of phones represented by HMM states. We find that features defined over the reduced space complement those previously known, such as, for example, phonological features. We are able to effectively combine all of these features in a phone recognition task by using the constraint-based framework of conditional random fields (CRFs), which allows the use of large and highly redundant feature spaces.